Does this program cover the cost of API access?

In Course 1 and 2, you will have access to optional activities where you will be guided to access Llama models via API. The cost of API credits is not included in this program and will be at your own cost. Some API providers may provide free credits when you first register with their platform. Please check with the providers directly to confirm.

Is this course really 100% online? Do I need to attend any classes in person?

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

Can I just enroll in a single course?

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Certificate, you’re automatically subscribed to the full Certificate. Visit your learner dashboard to track your progress.

Certificat Professionnel Building Generative AI Apps with Llama

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Certificat Professionnel Building Generative AI Apps with Llama

Create practical applications using Llama models..

Build and refine AI assistants using advanced prompting, fine-tuning and context integration.

Instructeur : Taught by Meta Experts

Inclus avec

niveau Avancées

Expérience recommandée

3 mois à compléter

à 8 heures par semaine

Planning flexible

Apprenez à votre propre rythme

niveau Avancées

Expérience recommandée

3 mois à compléter

à 8 heures par semaine

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

Build production-ready assistants with Llama models, applying best practices to design, test, and scale AI solutions for customer and business needs.
Develop advanced prompt engineering and fine-tuning techniques to shape assistant behavior, improve accuracy, and tailor outputs for real use cases.
Integrate Retrieval-Augmented Generation (RAG) and long-context to create assistants that deliver dynamic, informed, and multilingual responses.
Deploy assistants across local and cloud platforms, applying inference and quantization while building a portfolio of scalable, real-world projects.

Détails à connaître

Certificat partageable

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Enseigné en Anglais

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Pour quelles raisons les étudiants sur Coursera nous choisissent-ils pour leur carrière ?

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Étudiant(e) depuis 2018

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Étudiant(e) depuis 2020

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Foire Aux Questions

Developers, entrepreneurs, and technical professionals with 1-2 years of Python knowledge who want to build AI-enabled assistants. Ideal for those looking to upskill in generative AI development or create practical business solutions using Llama models.

1-2 years of Python programming experience (for those who need to meet this prerequisite, start with the Meta Programming in Python course)

Familiarity with command-line interfaces

Understanding of basic software development concepts

Basic knowledge of REST APIs

In this program, you will be guided to access the Llama 4 Scout 17B and Llama 3.1 8B models via API in Courses 1 and 2. The course content includes examples using one of the API providers, but you are free to choose any provider that offers access to Llama models for your learning experience. Examples of such providers include Together AI, Groq, Hugging Face, and others.

In Course 3, you will be guided to use the Llama 3.1 8B model in a local environment. Llama models are available from multiple sources, with the best place to start being https://www.llama.com.

Models are also hosted and distributed by partners such as Amazon Web Services, Microsoft Azure, Google Cloud, IBM Watsonx, Oracle Cloud, Snowflake, Databricks, Dell, Hugging Face, Groq, Cerebras, SambaNova, and many others. See the Llama.com FAQ for more information.

In Courses 1 and 2, you will access Llama models through an API. No additional device requirements are needed, but you will need internet access and to select your own API provider.

In Course 3, you will learn to use Llama in a local environment. Hardware requirements vary based on the specific Llama model being used, latency, throughput and cost constraints. For the larger Llama models to achieve low latency, one would split the model across multiple inference chips (typically a GPU) with tensor parallelism. Llama models are known to execute in a performant manner on a wide variety of hardware including GPUs, CPUs (both x86 and ARM based), TPUs, NPUs and AI Accelerators. The smaller Llama models typically run on system-on-chip (SOC) platforms found on PC, Mobile and other Edge devices.

See the Llama.com FAQ for more information.